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Learning with categorical performance metrics

Both situations with categorical and continuous, real-valued performance metrics will be considered and analyzed. Since Taguchi loss functions provide quality cost models that allow the different objectives to be expressed on a commensurate basis, for continuous performance variables only minor modifications in the problem definition of the approach presented in Section V are needed. On the other hand, if categorical variables are chosen to characterize the system s multiple performance metrics, important modifications and additional components have to be incorporated into the basic learning methodology described in Section IV. [Pg.129]


See other pages where Learning with categorical performance metrics is mentioned: [Pg.8]    [Pg.98]    [Pg.110]    [Pg.84]    [Pg.96]    [Pg.8]    [Pg.98]    [Pg.110]    [Pg.84]    [Pg.96]    [Pg.138]    [Pg.124]    [Pg.28]    [Pg.99]    [Pg.104]    [Pg.342]    [Pg.13]    [Pg.85]    [Pg.90]   


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Categorical

Categorization

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